Disk drive having built-in self-test system for characterizing performance of the drive

ABSTRACT

A disk drive has a normal mode of operation and a built-in self-test (BIST) mode of operation for producing a sequence of channel metrics {Γn}. The disk drive includes a recording surface having a plurality of bit cells and a transducer for reading the plurality of bit cells to produce a noise-corrupted read signal. The disk drive further includes means responsive to the noise-corrupted read signal for generating a sequence of observed samples {yn}, the sequence of observed samples {yn} forming a sequence of observed-sample subsequences {Yn}. An expected sample generator operates during the BIST mode of operation to provide a sequence of expected samples {wn}, the sequence of expected samples forming a sequence of expected-sample subsequences {Wn}. A channel metrics Γn computation system computes a sequence of channel metrics {Γn}. Each channel metric Γn is a function of a distance determined from one of the observed-sample subsequences Yn to the corresponding expected-sample subsequence Wn. Each channel metric Γn is independent of the earliest observed sample in every prior observed-sample subsequence Yn and the earliest expected sample in every prior expected-sample subsequence Wn.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a disk drive such as a magnetic harddisk drive. More particularly, the present invention relates to such adrive having a built-in self-test system for characterizing performanceof the drive.

2. Description of the Prior Art

A huge market exists for bard disk drives for mass-market host computersystems such as servers, desktop computers, and laptop computers. To becompetitive in this market, a hard disk drive must be relativelyinexpensive, and must accordingly embody a design that is adapted forlow-cost mass production In addition, it must provide substantialcapacity, rapid access to data, and reliable performance. Numerousmanufacturers compete in this huge market and collectively conductsubstantial research and development, at great annual cost, to designand develop innovative hard disk drives to meet increasingly demandingcustomer requirements.

An appreciable portion of such research and development has been, andcontinues to be, directed to developing effective and efficient ways toconduct, as part of the manufacturing process, unit-by-unit testing ofdrives. One aspect of such testing relates to determining the effectrandom noise has on drive performance. Data produced from such testingare useful in tuning processes directed to improving drive performance.Another aspect of such testing relates to determining the existence andlocation of defects such as defects in the media.

Random noise presents difficulties particularly in circumstances inwhich its magnitude is material in relation to the magnitude of asignal. In other words, a low signal-to-noise ratio (“S/N”) presentsproblems. In a drive, a low S/N presents problems in attempting toachieve high areal density. Areal storage density relates to the amountof data storage capacity per unit of area on the recording surfaces ofthe disks. The available areal density may be determined from theproduct of the track density measured radially and the linear bitdensity measured along the tracks.

The available linear bit density depends on numerous factors includingthe performance capability of certain circuitry that is commonlyreferred to as a “read channel.” One type of read channel is referred toas a peak-detecting channel; another type is referred to as asampled-data channel. The type referred to as a sampled-data channel isa category including a partial response, maximum likelihood (“PRML”)channel, a EPR4 channel, and a E²PR4 channel.

In a hard disk drive having any of these read channels, the read channelreceives an analog read signal from a transducer during a readoperation. The analog read signal is characterized by a “channelfrequency.” As used in this art, “channel frequency” is the reciprocalof a time period “T,” where the “T” is the time period consumed while anelemental-length magnet passes under the transducer during a readoperation with the disk spinning at a constant angular velocity. In thisregard, the length of each magnet recorded along a track as a result ofa write operation is, to a first order of approximation, either anelemental length or an integer multiple of the elemental length. Eachelemental length magnet can be referred to as a “bit cell” that isdefined during a write operation.

The analog read signal always contains some random noise. The analogread signal, and certain other signals produced by processing the analogread signal and that also contain noise, are referred to herein asnoise-corrupted signals. One such other noise-corrupted signal is asignal produced by filtering the analog read signal by means of alow-pass filter. Such filtering may reduce but not eliminate noise, andthe filtered signal is also noise corrupted. Further signal processingin the read channel provides for producing a digital signal comprisingdetected symbols, any of which can be in error in representing recovereddata. Such a digital signal is referred to herein as an error-pronesignal.

In a hard disk drive employing a peak detecting channel, digital dataare represented in the media by transitions between oppositelymagnetized bit cells. Provided that the transitions between oppositelymagnetized bit cells do not unduly interfere with each other, each suchtransition causes a peak in the analog read signal, and a peak-detectingchannel employs a peak detector that detects such peaks, and producesdigital signal in the form of a serial, binary-valued signal that is anerror-prone signal for numerous reasons. One reason why the peakdetector produces an error-prone signal is random noise; this source oferror presents a problem for any type of channel. Another reason relatesto interference between adjacent transitions. Interference between suchtransitions is referred to as intersymbol interference and adverselyaffects performance of a peak detecting channel increasingly as afunction of channel rate.

A sampled-data channel employs sampling circuitry that samples anoise-corrupted analog read signal to produce a sequence ofnoise-corrupted samples. The samples so produced are provided insequence to a detector. Such a detector may be organized such that itsdetection decisions are based on a sequence of the samples. Such adetector is sometimes called a “maximum likelihood sequence detector.” Aso-called “Viterbi detector” is an example of a maximum likelihoodsequence detector. In a sampled-data channel employing a Viterbidetector, circuitry responds to the noise-corrupted samples to produceerror-prone symbols and the produced error-prone symbols are mapped tobinary-valued error-prone symbols. In a PRML channel, suchinternally-produced error-prone symbols are often referred to as: “−1”;“0”; and “+1”; and the binary-valued error-prone symbols are supplied toa deserializer to produce a parallel-by-bit digital signal.

Prior art methods for characterizing the performance of a disk drive aretime consuming, costly, and inefficient. Prior art methods includevarious ways to perform test operations to produce either a bit errorrate (“BER”) or a histogram of noise magnitudes.

A BER of 10^(−x) means that, on the average, there is no more than oneerror per 10^(−x) bits. A raw BER for a disk drive is typically in therange of 10⁻⁶ BER to 10⁻¹⁰ BER. The raw BER is estimated without usingan ECC correction system to correct errors in a data sequence. A userBER is usually lower than the raw BER and is improved using the ECCcorrection system.

The BER can be used for fine tuning electronic components in the diskdrive. The BER test is commonly repeated after tuning the electroniccomponents.

Prior art methods for estimating the BER require a protracted readoperation that involves reading a large number of samples and countingthe number of bit errors that occur during the read operation. Prior artmethods commonly require reading 10⁸ samples to produce a reasonablyprecise estimate of BER when BER is in the neighborhood of 10⁻⁶ BER Thisis time consuming and inefficient. Other prior art methods require usinglarge and expensive test equipment to produce an estimate of the BER.This is costly as well as inefficient for use in the manufacturingenvironment.

U.S. Pat. No. 4,578,721 discloses a “window margin” method forestimating the bit error rate for disk drives employing peak detectionread channels. The “window margin” method is not suitable for diskdrives employing sampled data detection channels.

U.S. Pat. No. 5,355,261 discloses a method for estimating a BER for diskdrives having a partial response maximum likelihood data detectionchannel. This method requires comparing read back data bits and knowndata bits to count read back errors.

A publication titled “A WINDOW-MARGIN LIKE PROCEDURE FOR EVALUATING PRMLCHANNEL PERFORMANCE”, IEEE Transactions on Magnetics, Vol. 31, No.2,March 1995, discloses a method for estimating the BER that requirescounting read back errors during the read operation.

As for a test for measuring the performance of a disk drive bygenerating a histogram, U.S. Pat. No.5,121,263 discloses such a method.This patent teaches generating a histogram using the followingprocedures:

1. writing a pattern of binary data bits on the disk of the drive beingtested;

2. reading the data bits from the disk;

3. sampling the amplitude of the analog signal at recurring clockintervals;

4. comparing the sampled amplitude to reference amplitude values thatare expected to be received for each binary data bit that was recordedon the disk;

5. calculating a difference value for each binary bit that was stored onthe disk;

6. storing like magnitude difference values in one of a plurality ofregisters.

The count content of the plurality of registers provides a histogramdepicting the distribution of the like magnitude difference values. Theshape of the histogram characterizes the performance of the disk driveand provides a criterion for deciding whether the disk drive beingtested meets specifications.

There is a need for an efficient, accurate, and cost effective methodfor characterizing the performance of a disk drive in a manufacturingenvironment.

SUMMARY OF THE INVENTION

The invention can be regarded as a disk drive having a normal mode ofoperation and a built-in self-test mode of operation for producing asequence of channel metrics {Γ_(n)}. The disk drive includes a recordingsurface having a plurality of bit cells; a transducer for reading theplurality of bit cells to produce a noise-corrupted read signal; and ameans responsive to the noise-corrupted read signal for generating asequence of observed samples {y_(n)}. The sequence of observed samples{yn} forms a sequence of observed-sample subsequences {Y_(n)}. Eachobserved-sample subsequence Y_(n) has an earliest observed sample and alatest observed sample. The disk drive includes means operative duringthe built-in self-test mode of operation for providing a sequence ofexpected samples {w_(n)}. The sequence of expected samples forms asequence of expected-sample subsequences {W_(n)}. Each expected-samplesubsequence W_(n) has an earliest expected sample and a latest expectedsample. The disk drive includes computation means for computing asequence of channel metrics {Γ_(n)}. Each channel metric Γ_(n) is afunction of a distance determined from one of the observed-samplesubsequences Y_(n) to the corresponding expected-sample subsequenceW_(n). Each channel metric Γ_(n) is independent of the earliest observedsample in every prior observed-sample subsequence Y_(n) and the earliestexpected sample in every prior expected-sample subsequence W_(n).

In accordance with a feature of the invention, the disk drive furtherincludes means for computing a mean μ_(Γ) of the channel metrics Γ_(n);means for computing a standard deviation σ_(Γ) of the channel metricsΓ_(n); and means for computing a ratio of the mean μ_(Γ) to the standarddeviation σ_(Γ). The ratio corresponds to a signal to noise ratio.According to another feature of the invention, the disk drive includesmeans for estimating a BER from the ratio μ_(Γ)/σ_(Γ).

This invention can also be regarded as a method for computing a sequenceof channel metrics {Γ_(n)} for characterizing the performance of a diskdrive. This method include the steps of reading a plurality of bit cellsstored on a recording surface in the disk drive to produce anoise-corrupted read signal and generating a sequence of observedsamples {y_(n)} responsive to the noise-corrupted read signal. Thesequence of observed samples {y_(n)} forms a sequence of observed-samplesubsequences {Y_(n)}. Each observed-sample subsequence Y_(n) has anearliest observed sample and a latest observed sample. The methodincludes the step of providing a sequence of expected samples {w_(n)}.The sequence of expected samples forms a sequence of expected-samplesubsequences {W_(n)}. Each expected-sample subsequence W_(n) has anearliest expected sample and a latest expected sample. The methodincludes computing a sequence of channel metrics {Γ_(n)}. Each channelmetric Γ_(n) is a function of a distance determined from one of theobserved-sample subsequences Y_(n) to the corresponding expected-samplesubsequence W_(n). Each channel metric Γ_(n) is independent of theearliest observed sample in every prior observed-sample subsequenceY_(n) and the earliest expected sample in every prior expected-samplesubsequence W_(n).

In accordance with another feature of the invention, the method furtherincludes the steps of computing a mean μ_(Γ) of the channel metricsΓ_(n); computing a standard deviation σ_(Γ) of the channel metricsΓ_(n); and computing a ratio of the mean μ_(Γ) to the standard deviationσ_(Γ). The ratio corresponds to a signal to noise ratio. According toanother feature of the invention, the method further includes estimatingthe BER from the ratio μ_(Γ)/σ_(Γ).

The foregoing and other features of the invention are described indetail below and set forth in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a disk drive embodying the invention.

FIG. 2 is a block diagram of the HDA and channel components in the diskdrive of FIG. 1; the channel includes a built-in self-test system forcomputing a sequence of channel metrics {Γ_(n)}, and for accumulating asum of the channel metrics Γ_(n) (ΣΓ_(n)) and a sum of the squares ofthe channel metrics Γ_(n) (ΣΓ_(n) ²).

FIG. 3 is a trellis diagram showing a trellis path route correspondingto an expected-sample subsequence W_(n), the expected-sample subsequenceW_(n) being defined by an expected correct-sample subsequence R_(n) andan expected error-sample subsequence R′_(n).

FIG. 4A is a sequence diagram for a sampled signal produced by samplingand shows a sequence of noise corrupted unequalized samples u₀, . . .u₆.

FIG. 4B is a sequence diagram for a sampled signal that defines asequence of noise corrupted observed samples y₀, . . . y₆.

FIG. 4C is a sequence diagram for a sampled signal that defines anoiseless sequence of expected correct samples r₀, . . . ,r₆.

FIG. 5A is a trellis diagram showing an expected correct trellis pathcorresponding to the sequence of expected correct samples r₀, . . . ,r₆in FIG. 4C.

FIG. 5B is a set of six overlapping trellis diagrams that show asequence of trellis path routes corresponding to a sequence ofexpected-sample subsequences W₁, W₂, W₃, W₄, W₅, and W₆.

FIG. 6 is a signal space diagram illustrating two space distancesΓ_(R′1) and Γ_(R1) used in computing channel metric Γ₁.

FIG. 7 is a table that, for a set of conditions defined by a set ofexpected correct sample subsequences R_(n) and associated stateinformation s_(n), relates the conditions to a set of simplified channelmetric Γ_(n) equations that each is used for computing the channelmetric for the corresponding condition.

FIG. 8 is a block diagram of a channel metric Γ_(n) computation system,the channel metric Γ_(n) computation system including the set ofconditions defined by table 700 of FIG. 7 for selecting a channel metricΓ_(n) equation.

FIG. 9 is a flow chart showing the steps for estimating the BER for thedisk drive of FIG. 1.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring to FIG. 1, a disk drive 1 in accordance with a preferredembodiment of the invention includes a head disk assembly (“HDA 10”) anda printed circuit board assembly (“PCBA 12”). HDA 10 includes a disk 14having a recording surface 14 a for storing a plurality of bit cells, atransducer 20, and a preamp 22 coupled between transducer 20 and PCBA12. HDA 10 also includes a spindle motor 16 and a voice coil motor (“VCM18”). PCBA 12 includes a host interface and disk controller (“HIDC 32”)and a channel 26. Channel 26 participates in the transfer of data bitsbetween HIDC 32 and HDA 10. PCBA 12 also includes a microprocessor 34, adata buffer 42, a read only memory (“ROM 54”), a writeable random accessmemory (“RAM 60”), a spindle motor driver 56, and a VCM driver 58.

The disclosure of commonly owned co-pending U.S. patent application Ser.No. 08/815,352, filed Mar. 11, 1997, entitled DISK DRIVE EMPLOYING READERROR TOLERANT SYNC MARK DETECTION, is incorporated herein by reference(the “352 application”). A description of the elements shown in FIG. 1is set forth in the '352 application.

Disk drive 1 has a normal mode of operation and a built-in self-test(BIST) mode of operation. The BIST mode of operation has (a) a testwrite mode of operation and (b) a test read mode of operation.Alternatively, the BIST mode of operation may include the test read modeof operation only.

The BIST mode of operation can be used for estimating the BER of diskdrive 1 and locating defective sites on recording surface 14 a of disk14. The BIST mode of operation can also be used for tuning electroniccomponents in disk drive 1 to improve the BER.

During the BIST mode of operation, channel 26 computes a sequence ofchannel metrics {Γ_(n)} and accumulates a sum of the channel metricsΓ_(n) (ΣΓ_(n)) and a sum of the squares of the channel metrics Γ_(n)(ΣΓ_(n) ²). Channel 26 compares each channel metric Γ_(n) to a channelmetric Γ_(n) defect threshold. If the channel metric Γ_(n) is below thechannel metric Γ_(n) defect threshold, channel 26 generates a defectdiscovery signal indicating a defective site. The defect discoverysignal can be transmitted from channel 26 to HIDC 32 using a channeldata bus 38. The sequence of channel metrics {Γ_(n)} can be used forperforming a bit-by-bit defect discovery.

Microprocessor 34 receives the accumulated channel metrics Γ_(n) fromchannel 26 and computes a mean μ_(Γ) and a standard deviation σ_(Γ) ofthe channel metrics Γ_(n). Microprocessor 34 also computes a ratio ofthe mean μ_(Γ) to the standard deviation σ_(Γ), the ratio correspondingto a signal to noise ratio.

HIDC 32 receives and transmits the ratio (μ_(Γ)/σ_(Γ)) frommicroprocessor 34 to the host computer (not shown). Alternatively,microprocessor 34 estimates a BER from the ratio (μ_(Γ)/σ_(Γ)) Forexample, the BER can be estimated from Q (μ_(Γ)/σ_(Γ)), where Q is theGaussian Q function. HIDC 32 receives and transmits the BER frommicroprocessor 34 to the host computer (not shown). HIDC 32 receives thedefect discovery signal from channel 26 and records the defective sitein a defect list.

Referring to FIG. 2, channel 26 of FIG. 1 includes a pattern generator202, a write channel 204, a read channel 206, and a BIST mode testsystem 208. Read channel 206 includes a variable gain amplifier (“VGA212”), a low pass filter (“LPF 214”), a sampler 216, a sampled dataequalizer 218, a slicer detector 220, a timing control 222, a gaincontrol 224, a Viterbi detector 226, and a decoder 228. Write channel204 includes an encoder and a precoder (not shown).

Pattern generator 202 is a data source suitable for providing a datasignal 203 representing a test data sequence {b_(n)} to write channel204. As used in herein, for a sequence of elements having a lower caseletter and a subscript n, the subscript n represents the n^(th) elementin the sequence of elements.

For example, pattern generator 202 is a PN signal generator thatprovides a pseudo-random test data sequence {b_(n)}. The disclosure ofcommonly owned co-pending U.S. patent application Ser. No. 08/870,515,now U.S. Pat. No. 6,208,477, filed Jun. 6, 1997, entitled HARD DISKDRIVE HAVING A BUILT-IN SELF-TEST FOR MEASURING READBACK SIGNALDISTORTION, is incorporated herein by reference (the “515 application”).A description of a PN signal generator is set forth in the '515application.

Alternatively, pattern generator 202 can be a data source that isexternal to channel 26. For example, pattern generator 202 can be anexternal memory unit for providing a data signal 203 representing thetest data sequence {b_(n)}. The test data sequence {b_(n)} can be apredetermined test data sequence. Pattern generator 202 can also be ahost computer (user data) or a servo track writer (servo data) thatprovides a data signal 203 representing a non-test data sequence{b_(n)}.

BIST mode test system 208 includes an expected sample generator 230, achannel metric Γ_(n) computation and defect detection system 232 (“Γ_(n)computation system 232”), and a channel metric Γ_(n) accumulation system234. Alternatively, BIST mode system 208 includes channel metric Γ_(n)accumulation system 234 only. In this alternate embodiment, Viterbidetector 226 computes and transmits the sequence of channel metrics{Γ_(n)} to channel metric Γ_(n) accumulation system 234. Viterbidetector 226 can include a defect detection system for generating adefect discovery signal indicating a defective site for channel metricsΓ_(n) that do not meet a channel metric Γ_(n) defect threshold.

Write Mode of Operation

During the test write mode of operation, write channel 204 receives datasignal 203 representing the test data sequence {b_(n)} from patterngenerator 202 and produces a data signal 205 representing a test datasequence {b*_(n)). Alternatively, during the normal write mode ofoperation, write channel 204 receives a data signal 203 representing thenon-test data sequence {b_(n)} from pattern generator 202 and produces adata signal 205 representing a non-test data sequence {b*_(n)}.

Data signal 205 has a sequence of state information {s_(n)} thatcorresponds to the test data sequence {b*_(n)}. The precoder in writechannel 204 is suitable for generating data signal 205 representing thetest data sequence {b*_(n)}.

According to another embodiment, write channel 204 does not includes theencoder and the precoder. Write channel 204 receives data signal 203representing the test data sequence {b_(n)} from pattern generator 202and produces a data signal 205 without encoding and precoding signalprocessing.

Preamp 22 receives data signal 205 from write channel 204 and generateswrite signal 17 a corresponding to the test data sequence {b*_(n)}.Transducer 20 receives write signal 17 a and records the test datasequence {b*_(n)} as a plurality of bit cells on recording surface 14 a.

Read Mode of Operation

During the test read mode of operation, transducer 20 reads theplurality of bit cells stored on recording surface 14 a to produce anoise-corrupted analog read signal 17 b. Preamp 22 receives analog readsignal 17 b and produces a noise-corrupted analog read signal 211. VGA212 receives analog read signal 211 and under control of gain control224 produces an analog read signal 213 that has a substantially constantamplitude. LPF 214 receives analog read signal 213 and generates analogread signal 215 having an improved signal to noise ratio. Sampler 216receives analog read signal 215 and in response generates sampled signal217. Sampled data equalizer 218 receives sampled signal 217 andgenerates an equalized sampled signal 219 representing a sequence ofobserved samples {y_(n)}.

Slicer detector 220 receives equalized sampled signal 219 and inresponse generates a slicer sampled signal 221 representing a sequenceof slicer samples {a_(n)}. Slicer sampled signal 221 is a coarselyquantized estimate of equalized sampled signal 219. For example, slicersampled signal 221 for a PR4ML channel has one of three possible slicersample values (+1,0, −1). If the equalized sample value for equalizedsampled signal 219 is more positive than a first predetermined factor(e.g., ½) of the target value +1, the slicer sample value is +1. If theequalized sample value for equalized sampled signal 219 is more negativethan a second predetermined factor (e.g., ½) of the target value −1, theslicer sample value is −1. If the equalized sample value for equalizedsampled signal 219 is between (a) the first predetermined factor (e.g.,½) of the target value +1 and (b) the second predetermined factor (e.g.,½) of the target value −1, then the slicer sample value is 0.

Viterbi detector 226 receives equalized sampled signal 219 representingthe sequence of observed samples {y_(n)} and generates a data signal 227representing a data sequence {{circumflex over (b)}_(n)}. Decoder 228receives data signal 227 and generates a decoded data signal 229.

Expected sample generator 230 receives data signal 205 representing thetest data sequence {b*_(n)} that was supplied to preamp 22 during thetest write mode of operation. Expected sample generator 230 generates anexpected sample signal 231 representing a sequence of expected samples{w_(n)}. Expected sample generator 230 includes a finite state channelmodel (not shown) that receives state information {s_(n)} associatedwith the test data sequence {b*_(n)} to produce expected sample signal231. For example, the finite state channel model for disk drive 1 havinga PR4ML signal processing system is defined by the transfer polynomial(1-D²).

Alternatively, expected sample generator 230 receives signal 203representing the test data sequence {b_(n)} that was supplied to writechannel 204 during the test write mode of operation. In this alternativeembodiment, expected sample generator 230 includes an encoder, aprecoder and a finite state channel model. The encoder and precodergenerate the test data sequence {b*_(n)} corresponding to data signal205. The finite state channel model receives state information {s_(n)}associated with the test data sequence {b*_(n)} and generates signal 231representing the sequence of expected samples {w_(n)}.

According to alternate embodiment, the BIST mode of operation includesthe test read mode of operation only. Expected sample generator 230receives signal 221 representing the sequence of slicer samples {a_(n)}and generates signal 231 representing a sequence of expected samples{w_(n)}. In this alternate embodiment, the sequence of slicer samples{a_(n)} corresponds to the non-test data sequence {b*_(n)}. Expectedsample generator 230 includes a state machine (not shown) for definingstate information {s_(n)} associated with the non-test data sequence{b*_(n)}

State information {s_(n)} associated with the test data sequence{b*_(n)} defines the sequence of expected correct samples {Γ_(n)}.According to an alternate embodiment, state information {s_(n)}associated with the non-test data sequence {b*_(n)}defines the sequenceof expected correct samples {Γ_(n)}.

The sequence of expected error samples {r′_(n)} is defined by the stateinformation {s_(n)} associated with the sequence of expected correctsamples {Γr_(n)}. The sequence of expected samples {w_(n)} is defined bythe sequence of expected correct samples {Γr_(n)} and the sequence ofexpected error samples {r′_(n)}.

Channel metric Γ_(n) computation system 232 receives equalized sampledsignal 219 representing the sequence of observed samples {y_(n)} andsignal 231 representing the sequence of expected samples {w_(n)}.Channel metric Γ_(n) computation system 232 generates a signal 233representing a sequence of channel metrics {Γ_(n)}.

The sequence of observed samples {y_(n)} forms a sequence ofobserved-sample subsequences {Y_(n)}. Each observed-sample subsequenceY_(n) has an earliest observed sample and a latest observed sample. Thesequence of expected samples {w_(n)} forms a sequence of expected-samplesubsequences {W_(n)}. Each expected-sample subsequence W_(n) has anearliest expected sample and a latest expected sample. As used herein,for a sequence of subsequences having an upper case letter and asubscript n, the subscript n represents the n^(th) subsequence in thesequence of subsequences.

Each channel metric Γ_(n) is a function of a distance determined fromone of the observed-sample subsequences Y_(n) to the correspondingexpected-sample subsequence W_(n). Each channel metric Γ_(n) isindependent of the earliest observed sample in every priorobserved-sample subsequence Y_(n) and the earliest expected sample inevery prior expected-sample subsequence W_(n).

The distance determined from the observed-sample subsequences Y_(n) tothe corresponding expected-sample subsequence W_(n) corresponds to aspace distance, such as a Euclidean or Hamming distance. Alternatively,the distance can be an absolute value of the difference between theobserved-sample subsequence Y_(n) and expected-sample subsequence W_(n).

Channel metric Γ_(n) computation system 232 compares signal 233representing the channel metric Γ_(n) to a channel metric Γ_(n) defectthreshold. If signal 233 (channel metric Γ_(n)) is below the channelmetric Γ_(n) defect threshold, channel metric Γ_(n) computation system232 generates a defect discovery signal 238 indicating a defective siteassociated with the observed sample y_(n).

Channel metric Γ_(n) accumulation system 234 receives signal 233 andgenerates a signal 235 representing a sum of the channel metrics Γ_(n)(ΣΓ_(n)). Channel metric Γ_(n) accumulation system 234 also generates asignal 236 representing a sum of the squares of the channel metricsΓ_(n) (ΣΓ_(n) ²).

Microprocessor 34 receives signal 235 (representing the sum of thechannel metrics Γ_(n)) and signal 236 (representing the sum of thesquares of the channel metrics Γ_(n)) to compute the mean μ_(Γ) and thestandard deviation σ_(Γ). Microprocessor 34 also computes the ratio ofthe mean μ_(Γ) to the standard deviation σ_(Γ). The ratio represents thesignal to noise ratio. According to an alternate embodiment,microprocessor 34 estimates a BER from the ratio (μ_(Γ/σ) _(Γ))

HIDC 32 receives and transmits the ratio (μ_(Γ)/σ_(Γ)) frommicroprocessor 34 to the host computer (not shown). Alternatively, HIDC32 receives and transmits the BER from microprocessor 34 to the hostcomputer (not shown). HIDC 32 receives defect discovery signal 238 andrecords the defective site in the defect list.

Referring to FIG. 3, trellis diagram path route 300 corresponds to anexpected-sample subsequence W_(n) for disk drive 1 (FIG. 1) employing asampled data signal processing system tuned to a (1-D) channel response.This illustration is suitable for each of the (1-D) interleaves in aPR4ML signal processing system.

The expected-sample subsequence W_(n) is defined by an expectedcorrect-sample subsequence R_(n) and an expected error-samplesubsequence R′_(n). As previously mentioned, the sequence of expectedsamples {w_(n)} is defined by the sequence of expected correct samples{r_(n)} and the sequence of expected error samples {r′_(n)}. Thesequence of expected correct samples {r_(n)} forms the sequence ofexpected correct-sample subsequences {R_(n)}. Each expectedcorrect-sample subsequence R_(n) has an earliest expected correct sampleand a latest expected correct-sample. The sequence of expected errorsamples {r′_(n)} forms the sequence of expected error-samplesubsequences {R′_(n)}. Each expected error-sample subsequence R′_(n) hasan earliest expected error sample and a latest expected error sample.

Path route 300 has a correct path 302 corresponding to the expectedcorrect-sample subsequence R_(n) and an error event path 304corresponding to the expected error-sample subsequence R′_(n). Correctpath 302 begins from a beginning state s_(n−2) 306 and ends at an endingstate s_(n) 308. Error event path 304 is a minimum distance error eventpath that begins from the beginning state s_(n−2) 306 and ends at theending state 308 s_(n).

Path route 300 has two time steps between the beginning state s_(n−2)306 and the ending state 308 s_(n). Alternatively, path route 300 hasmore than two times steps between the beginning state 306 and the endingstate 308. The number of time steps corresponds to the number of samplesbetween the beginning state 306 and ending state 308.

Example

The following is an example of disk drive 1 (FIG. 1) employing a sampleddata signal processing system tuned to a (1-D) channel response. Thisexample is also suitable for each of the (1-D) interleaves in a PR4MLsignal processing system. The mean μ_(Γ) of the channel metrics {Γ_(n)}is computed for each interleave and then combined together. The standarddeviation of the channel metrics {Γ_(n)} is also computed for eachinterleave and then combined together.

During the test write mode of operation, write channel 204 receives atest data sequence b₀=1, b₁=0, b₂=0, b₃=0, b₄=0, b₅=1, and b₆=0 andgenerates a test data sequence b*_(initial)=0, b*₀=1, b*₁=1, b*₂=1,b*₃=1, b*₄=1, b*₅=0, and b*₆=0. Transducer 20 receives state informations_(initial)=−, s₀=+, s₁=+, s₂=+, s₃=+, s₄=+, s₅=−, and s₆=− associatedwith the test data sequence {b*_(n)} and records the test data sequence{b*_(n)} as a plurality of bit cells on recording surface 14 a of disk14.

During the test read mode of operation, transducer 20 reads theplurality of bit cells stored on recording surface 14a to produce anoise-corrupted read signal. Sampler 216 generates a noise corruptedsequence of unequalized samples u₀=+0.8, u₁=−0.1, u₂=−0.8, u₃=−0.3,u₄=−0.4, u₅=−1.7, and u₆=−0.9 responsive to the noise-corrupted readsignal. Referring to FIG. 4A, sequence diagram 402 shows the noisecorrupted sequence of unequalized samples u₀, u₁, u₂, u₃, u₄, u₅, andu₆.

Sampled data equalizer 218 generates a sequence of observed samplesy₀=+0.8, y₁=−0.1, y₂=−0.6, y₃=+0.1, y₄=+0.3, y₅=−1.2, and y₆=−0.2responsive to the noise corrupted sequence of unequalized samples.Referring to FIG. 4B, sequence diagram 404 shows the sequence ofobserved samples y₀, y₁, y₂, y₃, y₄, y₅ and y₆. The sequence of observedsamples {y_(n)} includes noise which contributes to sample valuesdeviating from their expected correct values.

The sequence of observed samples {y_(n)} forms a sequence ofobserved-sample subsequences Y_(n): Y₁=y₀,y₁; Y₂=y₁,y₂; Y₃=y₂,y₃;Y₄=y_(3,y) ₄; Y₅=y₄,y₅; and Y₆=y₅,y₆. Each observed-sample subsequenceY_(n) has an earliest observed sample and a latest observed sample.

Expected sample generator 230 receives the test data sequenceb*_(initial)=0, b*₀=1, b*₁=1, b*₂=1, b*₃=1, b*₄=1, b*₅=0, and b*₆=0 andprovides a corresponding sequence of expected correct samples r₀=+1.0,r₁=0.0, r₂=0.0, r₃=0.0, r₄=0.0, r₅=−1.0, and r₆=0.0. State information{s_(n)} associated with the test data sequence {b*_(n)} defines thesequence of expected correct samples {r_(n)}. Referring to FIG. 4C,sequence diagram 406 shows the sequence of expected correct samples r₀,r₁, r₂, r₃, r₄, r₅, and r₆. The sequence of expected correct samples{r_(n)} represents noiseless samples having expected values.

The sequence of expected correct samples {Γr_(n) } forms a sequence ofexpected correct-sample subsequences R_(n): R₁=r₀r₁; R₂=r₁,r₂; R₃=r₂,r₃;R₄=r₃,r₄; R₅=r₄,r₅; and R₆=r₅,r₆. Each expected correct-samplesubsequence R_(n) has an earliest expected correct sample and a latestexpected correct sample. The sequence of expected correct-samplesubsequences R_(n) defines a sequence of expected error-samplesubsequences R′_(n): R′₁,=r′₀,r′₁; R′₂r′₁,r′₂; R′₃=r′₂,r′₃; R′₄=r′₃,r′₄;R′₅=r′₄,r′₅; and R′₆=r′₅,r′₆. Each expected error-sample subsequenceR′_(n) has an earliest expected error sample and a latest expected errorsample.

Referring to FIG. 5A, trellis diagram 500 includes an expected correcttrellis path 502 corresponding to the sequence of expected correctsamples r₀=+1.0, r₁=0.0, r₂=0.0, r₃=0.0, r₄=0.0, r₅=−1.0, and r₆=0.0.Expected correct trellis path 502 defines a sequence of six overlappingpath routes 504, 506, 508, 510, 512, and 514. Each path routecorresponds to an expected-sample subsequence W_(n).

Referring to FIG. 5B, trellis diagram 516 shows the sequence ofoverlapping path routes 504, 506, 508, 510, 512, and 514 correspondingto a sequence of expected-sample subsequences W_(n): W₁, W₂, W₃, W₄, W₅and W₆. As previously mentioned, each expected-sample subsequence W_(n)is defined by an expected correct-sample subsequence R_(n) and anexpected error-sample subsequence R′_(n). As shown in trellis diagram516, each path route includes a correct path (solid line) correspondingto an expected correct-sample subsequence R_(n): r_(n−1),r_(n) and anerror event path (dotted line) corresponding to an expected error-samplesubsequence R′_(n): r′_(n−1),r′_(n).

Referring to FIGS. 5A and 5B, path route 504 corresponds to theexpected-sample subsequence W₁. The expected-sample subsequence W₁ isdefined by expected correct-sample subsequence R₁: r₀=+1.0, r₁=0.0, andexpected error-sample subsequence R′₁: r′₀=0.0, r′₁=+1.0. Path route 506corresponds to expected-sample subsequence W₂. The expected-samplesubsequence W₂ is defined by expected correct-sample subsequence R₂:r₁=0.0, r₂=0.0, and expected error-sample subsequence R′₂: r′₁=−1.0,r′₂=+1.0. Path route 508 corresponds to expected-sample subsequence W₃.The expected-sample subsequence W₃ is defined by expected correct-samplesubsequence R₃: r₂0.0, r₃=0.0, and expected error-sample subsequenceR′₃: r′₂=−1.0, r′₃=+1.0. Path route 510 corresponds to expected-samplesubsequence W₄. The expected-sample subsequence W₄ is defined byexpected correct-sample subsequence R₄: r₃=0.0, r₄=0.0, and expectederror-sample subsequence R′₄: r′₃=−1.0, r′₄=+1.0. Path route 512corresponds to expected-sample subsequence W₅. The expected-samplesubsequence W₅ is defined by expected correct-sample subsequence R₅:r₄=0.0, r₅=−1.0, and expected error-sample subsequence R′₅: r′₄=−1.0,r′₅=0.0. Path route 514 corresponds to expected-sample subsequence W₆.The expected-sample subsequence W₆ is defined by expected correct-samplesubsequence R₆: r₅=−1.0, r₆=0.0, and expected error-sample subsequenceR′₆: r′₅=0.0, r′₆=−1.0.

Table 1 shows sample values for the sequence of observed-samplesubsequences {Y_(n): y_(n−1),y_(n)} and the sequence of expected-samplesubsequences {W_(n)}, wherein the sequence of expected-samplesubsequences {W_(n)} is defined by the sequence of expectedcorrect-sample subsequences {R_(n): r_(n−1),r_(n)} and the sequence ofexpected error-sample subsequences (R′_(n): r′_(n−1),r′_(n)}.

TABLE 1 observed-sample expected-sample sub- subsequence Y_(n)subsequence W_(n) sequence Y_(n): y_(n−1),y_(n) R_(n): r_(n−1),r_(n)R′_(n): r′_(n−1),r′_(n) n = 1 y₀ = +0.8, y₁ = −0.1 r₀ = +1.0, r₁ = 0.0r′₀ = 0.0, r′₁ = +1.0 n = 2 y₁ = −0.1, y₂ = −0.6 r₁ = 0.0, r₂ = 0.0 r′₁= −1.0, r′₂ = +1.0 n = 3 y₂ = −0.6, y₃ = +0.1 r₂ = 0.0, r₃ = 0.0 r′₂ =−1.0, r′₃ = +1.0 n = 4 y₃ = +0.1, y₄ = +0.3 r₃ = 0.0, r₄ = 0.0 r′₃ =−1.0, r′₄ = +1.0 n = 5 y₄ = +0.3, y₅ = −1.2 r₄ = 0.0, r₅ = −1.0 r′₄ =−1.0, r′₅ = 0.0 n = 6 y₅ = −1.2, y₆ = −0.2 r₅ = 1.0, r₆ = 0.0 r′₅ = 0.0,r′₆ = −1.0

The channel metric Γ_(n) can be determined from the equation[(y_(n−1)−r′_(n−1))²+(y_(n)−r′_(n))²]−[(y_(n−1)−r_(n−1))²+(y_(n)−r_(n))²].The channel metric equation includes a channel metric component Γ_(R′n)corresponding to [(y_(n−1)−r′_(n−1))²+(y_(n)−r′_(n))²] and a channelmetric component Γ_(Rn) corresponding to[(y_(n−1)−r_(n−1))²+(y_(n)−r_(n))²]. Alternatively, the channel metricΓ_(n) can be determined from other simplified equations derived from thechannel metric Γ_(n) equation[y_(n−1)−r′_(n−1))²+(y_(n)−r′_(n))²]−[(y_(n−1)−r_(n−1))²+(y_(n)−r_(n))₂].

TABLE 2 sample n initial state n = 0 n = 1 n = 2 n = 3 n = 4 n = 5 n = 6test data sequence {b_(n)} 1 0 0 0 0 1 0 test data sequence {b*_(n)} 0 11 1 1 1 0 0 observed sample sequence {y_(n)} +0.8 −0.1 −0.6 +0.1 +0.3−1.2 −0.2 expected sample sequence {r_(n)} +1.0 0.0 0.0 0.0 0.0 −1.0 0.0expected state information {s_(n)} − + + + + + − − channel metrics{Γ_(n)} 1.80 3.00 .60 1.60 3.00 5.40 Viterbi state metric (+1 state) .04.05 .41 .42 .51 1.95 1.99 Viterbi state metric (−1 state) 0 .64 .65 .21.22 .31 .55 .59

Channel metric Γ_(n) computation system 232 receives one of theobserved-sample subsequences Y_(n): y_(n−1),y_(n), the correspondingexpected correct-sample subsequence R_(n): r_(n−1),r_(n), and thecorresponding expected correct-sample subsequence R′_(n):r′_(n−1),r′_(n) to compute the channel metric Γ_(n) . Alternatively,channel metric Γ_(n) computation system 232 receives one of theobserved-sample subsequences Y_(n): y_(n−1),y_(n) the correspondingexpected correct-sample subsequence R_(n): r_(n−1),r_(n), and thecorresponding state information s_(n) associated with the expectedcorrect-sample subsequence R_(n).

Referring to tables 1 and 2, channel metric Γ₁ (table 2, at column n=1)is 1.80 and represents a distance determined from the expected-observedsample subsequence Y₁: y₀=+0.8,y₁=−0.1 to the corresponding expectedcorrect-sample subsequence W₁. Channel metric Γ₂ (at column n=2) is 3.00and represents a distance determined from the expected-observed samplesubsequence Y₂: y₁=−0.1,y₂=−0.6 to the corresponding expectedcorrect-sample subsequence W₂. Channel metric Γ₃ (at column n=3) is 0.60and represents a distance determined from the expected-observed samplesubsequence Y₃: y₂=−0.6,y₃=+0.1 to the corresponding expectedcorrect-sample subsequence W₃. Channel metric Γ₄ (at column n=4) is 1.60and represents a distance determined from the expected-observed samplesubsequence Y₄: y₃=+0.1, y₄=+0.3 to the corresponding expectedcorrect-sample subsequence W₄. Channel metric Γ₅ (at column n=5) is 3.00and represents a distance determined from the expected-observed samplesubsequence Y₅: y₄=+0.3,y₅=−1.2 to the corresponding expectedcorrect-sample subsequence W₅. Channel metric Γ₆ (at column n=6) is 5.40and represents a distance determined from the expected-observed samplesubsequence Y₆: y₅=−1.2,y₆=−0.2 to the corresponding expectedcorrect-sample subsequence W₆.

Referring to FIG. 6, signal space 600 illustrates the space distancecorresponding to channel metric Γ₁. Signal space 600 includes possibleexpected sample points 602 (0.0,+1.0), 604 (−1.0,+1.0), 606 (−1.0,0.0),608 (0.0,0.0), 610 (+1.0,0.0), 612 (0.0,−1.0), and 614 (+1.0,−1.0). Eachpossible expected sample point corresponds to a possible expectedcorrect-sample subsequence R_(n): r_(n−1),r_(n) or a possibleexpected-error sample subsequence R′_(n): r′_(n−1),r′_(n).

For example, sample point 616 corresponds to the observed-samplesubsequence Y₁: y₀=+0.8,y₁=−0.1. The channel metric Γ₁ (table 2, atcolumn n=1) is 1.80 and represent a distance determined from theexpected-observed sample subsequence Y₁ to the corresponding expectedcorrect-sample subsequence W₁. The expected correct-sample subsequenceW₁ is defined by the expected correct-sample subsequence R₁:r₀=+1.0,r₁=0.0 associated with the correct path in path route 504, andthe expected error-sample subsequence R′₁: r′₀=0.0,r′₁=+1.0 associatedwith the error event path in path route 504.

Sample point 602 corresponds to the expected error-sample subsequenceR′₁: r′₀=0.0,r′₁=+1.0. Sample point 610 corresponds to the expectedcorrect-sample subsequence R₁: r₀=+1.0,r=0.0. Space distance 618represents the channel metric component Γ_(R′1) (1.85). Space distance620 represents the channel metric component Γ_(R1) (0.05). The channelmetric Γ₁ (1.8) represents a distance determined from space distance 618(Γ_(R′1)=1.85) minus space distance 620 (Γ_(R1)=0.05).

Table 2 shows a Viterbi state metric at a +1 state and a −1 state. TheViterbi state metric is computed for each state beginning from aninitial state and continuing to a state at n=6. The initiate states_(initial) is—and the Viterbi state metric for the −1 state is 0.

Viterbi detector 220 has a structure defining a Viterbi engine forcomputing the +1 Viterbi state metric and the −1 Viterbi state metric.The Viterbi detector also has a path memory for retaining and generatinga trellis path having the least accumulated state metrics. The Viterbiengine operates in a repeated cycle. During each cycle, it computes twoViterbi state metrics for each of the two respective states (+1 stateand −1 state). The Viterbi engine computes a branch metric for eachpossible path entering each state, adds the branch metric to a previousViterbi state metric to produce a candidate Viterbi state metric, andselects the candidate Viterbi state metric having the lowest value.

During each cycle, the path memory is updated so that it selects a pathhaving the lowest accumulated Viterbi state metric. The Viterbi enginecomputes each branch metric by computing the square of the differencebetween the value of an observed sample y_(n) and the value of apossible target sample t_(n). The branch metric equation is(y_(n)−t_(n))². Viterbi detector 220 includes branch metric registersfor holding computed branch metrics and state metric registers forholding accumulated Viterbi state metrics.

The selected Viterbi state metric is a sum of a branch metric and aprevious state metric having the least accumulated Viterbi state metric.The selected Viterbi state metric depends on the earliest observedsample in the observed sequence and the earliest target sample in thepossible sample sequence.

Table 3 differs from table 1 in that the earliest observed sample y₀ ischanged from +0.8 to +0.3. Channel metric Γ₁ (at column n=1) decreasedfrom 1.8 to 0.8. The remaining channel metrics Γ₂ through Γ₆ are notaffected by this change in the earliest observed sample y₀. Channelmetrics Γ₂ through Γ₆ are independent of the earliest observed sampley₀. Each channel metric Γ_(n) is independent of the earliest observedsample in every prior observed-sample subsequence Y_(n) and the earliestexpected sample subsequence in every prior expected sample-subsequenceW_(n).

Each Viterbi state metric (columns n=0 through n=6) is affected bychanging the earliest observed sample y₀. Each Viterbi state metric (+1state and −1 state) shown in columns n=1 through n=6 depends on theearliest observed sample y₀.

TABLE 3 sample n initial state n = 0 n = 1 n = 2 n = 3 n = 4 n = 5 n = 6test data sequence {b_(n)} 1 0 0 0 0 1 0 test data sequence {b*_(n)} 0 11 1 1 1 0 0 observed sample value {y_(n)} +0.3 −0.1 −0.6 +0.1 +0.3 −1.2−0.2 expected sample value {r_(n)} +1.0 0.0 0.0 0.0 0.0 −1.0 0.0expected state information {s_(n)} − + + + + + − − channel metrics{Γ_(n)} .80 3.00 .60 1.60 3.00 5.40 Viterbi state metric (+1 state) .49.50 .86 .87 .96 2.4 2.44 Viterbi state metric (−1 state) 0 .09 .10 .46.47 .56 1.00 1.04

Channel metric Γ_(n) computation system 232 compares each channel metricΓ_(n) to a channel metric Γ_(n) defect threshold. For example, if thechannel metric Γ_(n) defect threshold is 1, channel metric Γ_(n)computation system 232 will generate a defect discovery signal forchannel metric Γ₃. The defect discovery signal indicates a defectivesite associated with observed samples Y2 and y₃.

Channel metric Γ_(n) accumulation system 234 receives and accumulates asequence of the channel metrics {Γ_(n)} to produce a sum of the channelmetrics ΣΓ_(n) and a sum of the squares of the metrics ΣΓ_(n) ².

Microprocessor 34 receives the sum of the channel metrics Γ_(n) and thesum of the squares of the channel metrics Γ_(n) to compute the meanμ_(Γ) and the standard deviation σ_(Γ). Microprocessor 34 also computesthe ratio of the mean μ_(Γ) to the standard deviation σ_(Γ). The ratiorepresents the signal to noise ratio. According to an alternateembodiment, microprocessor 34 estimates a BER from the ratio(μ_(Γ)/σ_(Γ)).

HIDC 32 receives and transmits the ratio (μ_(Γ)/σ_(Γ)) frommicroprocessor 34 to the host computer (not shown). Alternatively, HIDC32 receives and transmits the BER from microprocessor 34 to the hostcomputer (not shown). HIDC 32 receives the defect discovery signal andrecords the defective site in the defect list.

Referring to FIG. 7, table 700 shows eight simplified channel metricΓ_(n) equations 706 corresponding to eight possible trellis path routes708. Each channel metric Γ_(n) equation 706 includes a constant C havinga value equal to +2, 0 or −2; a y_(n−1) coefficient having a value equalto +2 or −2; and a y_(n) coefficient having a value equal to +2 or −2.Channel metric Γ_(n) equations 706 are derived from the equation[(y_(n−1)−r′_(n−1))²+(y_(n)−r′_(n))²]−[(Y_(n−1)−r_(n−1))²+(y_(n)−r_(n))²].

Column 702 provides a list of four possible expected-correct samplesubsequences R_(n)(r_(n−1), r_(n)) for each state (s_(n)=+, s_(n)=−)Column 704 provides state information s_(n) associated with each of thepossible expected-correct sample subsequences R_(n)(r_(n−1), r_(n)). Theexpected-correct sample subsequences R_(n)(r_(n−1), r_(n)) 702 and stateinformation s_(n) (−or+) 704 define a set of eight conditions, eachcondition being associated with a corresponding channel metric equationΓ_(n) 706.

Referring to FIG. 8, channel metric Γ_(n) computation system 232computes a channel metric Γ_(n) in accordance with the set of conditionsdefined in table 700 of FIG. 7. Channel metric Γ_(n) computation system232 includes a selection matrix 250, a first selector 252 for selectinga constant C having a value equal to +2,0,or 2, a second selector 254for selecting a y_(n−1) coefficient having a value equal to +2 or −2, athird selector 256 for selecting a y_(n) coefficient having a valueequal to +2 or −2, a first multiplier 258, a second multiplier 260, anda summer 262.

Equation selection matrix 250 receives an expected correct-samplesubsequence R_(n)(r_(n−1),r_(n)) and state information s_(n). Equationselection matrix 250 generates a first signal 264 representing theconstant C, a second signal 266 representing the y_(n−1) coefficient,and a third signal 268 representing the y_(n) coefficient.

First selector 252 receives first signal 264 from equation selectionmatrix 250 and produces a signal 270 representing the selected constantC (+2,0,or −2). Second selector 254 receives second signal 266 fromequation selection matrix 250 and produces a signal 272 representing they_(n−1) coefficient (+2 or −2). Third selector 256 receives third signal268 from equation selection matrix 252 and produces a signal 274representing the y_(n) coefficient (+2 or −2).

First multiplier 258 receives an observed sample Y_(n−1) and signal 272representing the y_(n−1) coefficient (+2 or −2) to produce a signal 276representing a product of the observed sample y_(n−1) and the y_(n−1)coefficient (+2 or −2). Second multiplier 260 receives an observedsample y_(n) and signal 274 representing the y_(n) coefficient (+2 or−2) to produce a signal 278 representing a product of the observedsample y_(n) and the y_(n) coefficient (+2 or −2).

Summer 262 receives signal 270 representing the selected constant C(+2,0,or −2), signal 276 representing the product of the observed sampley_(n−1) and the Y_(n−1) coefficient (+2 or −2), and signal 278representing the product of the observed sample y_(n) and the y_(n)coefficient (+2 or −2) to produce a signal 280 representing the channelmetric Γ_(n).

Continuing the above example, channel metric Γ_(n) computation system232 receives the observed-sample subsequence Y₁ (y₀=+0.8, y₁=−0.1), theexpected correct-sample subsequence R₁(r₀=+1,r₁=0), and stateinformation s₁=+ associated with the expected-correct sample r₁=0. TheR_(n) and s_(n) conditions corresponds to the channel metric Γ_(n)equation 2(y_(n−1)−y_(n)) in row 6 of table 700.

Equation selection matrix 250 receives the expected correct-samplesubsequence R₁ (r₀=+1,r₁=0) and state information s₁=+. Equationselection matrix 250 generates first signal 264 representing theconstant C having a value equal to 0, a second signal 266 representingthe Y_(n−1) coefficient having a value equal to +2, and a third signal266 representing the y_(n) coefficient having a value equal to −2.

First selector 252 receives first signal 264 from equation selectionmatrix 250 and produces a signal 270 representing the selected constantC (0). Second selector 254 receives second signal 266 from equationselection matrix 250 and produces a signal 272 representing the y_(n−1)coefficient (+2). Third selector 256 receives third signal 268 fromequation selection matrix 250 and produces a signal 274 representing they_(n) coefficient (−2).

First multiplier 258 receives the observed sample y₀=+0.8 and signal 272representing the y_(n−1) coefficient (+2) to produce a signal 276representing a value equal to +1.6. Second multiplier 260 receives theobserved sample −0.1 and signal 274 representing the y_(n) coefficient(−2) to produce a signal 278 representing a value equal to +0.2.

Summer 262 receives signal 270 representing the selected constant havinga value equal to 0, signal 276 representing the value equal to +1.6, andsignal 278 representing the value equal to +0.2 to produce a signal 280representing the channel metric Γ₁ having a value equal to 1.8.

According to another embodiment, computation circuitry in Viterbidetector 226 can be used for computing each channel metric Γ_(n).Viterbi detector 226 receives the sequence of observed samples {y_(n)}and state information {s_(n)} associated with the sequence of expectedsamples {w_(n)} to produce the sequence of channel metrics {Γ_(n)}. Inthis embodiment, BIST mode test system 208 includes the channel metricΓ_(n) accumulation system 234 only. Channel metric Γ_(n) accumulationsystem 234 receives the sequence of channel metrics {Γ_(n)} from Viterbidetector 226 and accumulates a sum of the channel metrics Γ_(n) (ΣΓ_(n))and a sum of the squares of the channel metrics Γ_(n) (ΣΓ_(n) ²).

Referring to FIG. 9, flow chart 900 describes a method for computing asequence of channel metrics {Γ_(n)} for characterizing the performanceof a disk drive (such as disk drive 1 of FIG. 1). At step 902, atransducer (such as 20 of FIG. 1) reads a plurality of bit cells storedon a recording surface in the disk drive to produce a noise-corruptedread signal. At step 904, a sampled data equalizer (such as 218 of FIG.2) generates a sequence of observed samples {y_(n)} responsive to thenoise-corrupted read signal. The sequence of observed samples{y_(n)}forms a sequence of observed-sample subsequences {Y_(n)}. Eachobserved-sample subsequence Y_(n) has an earliest observed sample and alatest observed sample.

At step 906, an expected sample generator (such as 230 of FIG. 2)provides a sequence of expected samples {w_(n)}. The sequence ofexpected samples forms a sequence of expected-sample subsequences{W_(n)}. Each expected-sample subsequence W_(n) has an earliest expectedsample and a latest expected sample. At steps 908 and 910, a channelmetric Γ_(n) computation system (such as 232 of FIG. 2) computes asequence of channel metrics {Γ_(n)}, and a channel metric Γ_(n)accumulation system (such as 234 of FIG. 2) accumulates a sum of thechannel metrics Γ_(n) (ΣΓ_(n)) and a sum of the squares of the channelmetrics Γ_(n) (ΣΓ_(n)). Each channel metric Γ_(n) is a function of adistance determined from one of the observed-sample subsequences Y_(n)to the corresponding expected-sample subsequence W_(n). Each channelmetric Γ_(n) is independent of the earliest observed sample in everyprior observed-sample subsequence Y_(n) and the earliest expected samplein every prior expected-sample subsequence W_(n).

At step 912, a processor (such as 34 of FIG. 1) computes a mean μ_(Γ) ofthe accumulated channel metrics {Γ_(n)}. At step 914, the processorcomputes a standard deviation σ_(Γ) of the accumulated channel metrics{Γ_(n)}. At step 916, the processor computes a ratio of the mean μ_(Γ)to the standard deviation σ_(Γ). The ratio corresponds to a signal tonoise ratio. Alternatively, at step 918, the processor estimates the BERfrom the ratio (μ_(Γ)/σ_(Γ)) For example, the BER can be estimated fromQ (μ_(Γ)/σ_(Γ)), where Q is the Gaussian Q function.

The ratio (μ_(Γ)/σ_(Γ)) constitutes a very precise basis forcharacterizing the performance of disk drive 1 because the ratio isbased on computing multiple channel metrics Γ_(n) for a sequence ofobserved sample {y_(n)}. The ratio (μ_(Γ)/σ_(Γ)) provides for rapidlycharacterizing the performance of disk drive 1. The ratio (μ_(Γ)/σ_(Γ))can be used for rapidly and precisely estimating the BER for disk drive1. For example, about 10³ observed samples can produce a preciseestimate of BER when the BER is in the neighborhood of 10⁻⁶ BER. Thesequence of channel metrics {Γ_(n)} can be used for discoveringdefective sites on recording surface 14 a of disk drive 1.

We claim:
 1. A disk drive having a normal mode of operation and abuilt-in self-test mode of operation for producing a sequence of channelmetrics {Γ_(n)}, the disk drive comprising: a recording surface having aplurality of bit cells; a transducer for reading the plurality of bitcells to produce a noise-corrupted read signal; means responsive to thenoise-corrupted read signal for generating a sequence of observedsamples {y_(n)}, the sequence of observed-samples {y_(n)} forming asequence of observed-sample sequences {Y_(n)}, each observed samplesequence Y_(n) having an earliest observed sample and a latest observedsample; means operative during the built-in self-test mode of operationfor providing a sequence of expected samples {w_(n)}, the sequence ofexpected samples forming a sequence of expected-sample subsequences{W_(n)}, each expected-sample subsequence W_(n) having an earliestexpected sample and a latest expected sample; computation means forcomputing a sequence of channel metrics {Γ_(n)}, each channel metricΓ_(n) being a function of a distance determined from one of theobserved-sample subsequences Y_(n) to the corresponding expected-samplesequence W_(n), each channel metric Γ_(n) being independent of theearliest observed sample in every prior observed-sample subsequenceY_(n) and the earliest expected sample in every prior expected-samplesubsequence W_(n); means for computing a mean μ_(Γ) of the channelmetrics Γ_(n); means for computing a standard deviation σ_(Γ) of thechannel metrics Γ_(n); and means for computing a ratio of the mean μ_(Γ)to the standard deviation σ_(Γ), the ratio (μ_(Γ)/σ_(Γ)) correspondingto a signal to noise ratio.
 2. The disk drive of claim 1 furthercomprising means for estimating a bit error rate of the disk drive fromthe ratio (μ_(Γ)/σ_(Γ)).
 3. The disk drive of claim 2, wherein the meansfor estimating the bit error rate comprises a means for computingQ(μ_(Γ)/σ_(Γ)) where Q is a Gaussian Q function.
 4. A method forcomputing a sequence of channel metrics {Γ_(n)} for characterizing theperformance of a disk drive, the method comprising the steps of: readinga plurality of bit cells stored on a recording surface in the disk driveto produce a noise-corrupted read signal; generating a sequence ofobserved samples {y_(n)}, the sequence of observed-samples {y_(n)}forming a sequence of observed-sample sequences {Y_(n)}, each observedsample sequence Y_(n) having an earliest observed sample and a latestobserved sample; providing a sequence of expected samples {w_(n)}, thesequence of expected samples forming a sequence of expected-samplesubsequences {W_(n)}, each expected-sample subsequence W_(n) having anearliest expected sample and a latest expected sample; computing asequence of channel metrics {Γ_(n)}, each channel metric Γ_(n) being afunction of a distance determined from one of the observed-samplesubsequences Y_(n) to the corresponding expected-sample sequence W_(n),each channel metric Γ_(n) being independent of the earliest observedsample in every prior observed-sample subsequence Y_(n) and the earliestexpected sample in every prior expected-sample subsequence W_(n); andcomputing a mean μ_(Γ) of the channel metrics Γ_(n); computing astandard deviation σ_(Γ) of the channel metrics Γ_(n); computing a ratioof the mean μ_(Γ) to the standard deviation σ_(Γ), the ratio(μ_(Γ)/σ_(Γ)) corresponding to a signal to noise ratio.
 5. The method ofclaim 4 further comprising the step of estimating a bit error rate ofthe disk drive from the ratio (μ_(Γ)/σ_(Γ)).
 6. The disk drive of claim5, wherein the step of estimating the bit error rate comprises the stepof computing Q(μ_(Γ)/σ_(Γ)) where Q is a Gaussian Q function.
 7. Amethod for estimating a bit error rate for a disk drive, the methodcomprising the steps of: reading a plurality of bit cells stored on arecording surface in the disk drive to produce a noise-corrupted readsignal; generating a sequence of observed samples {y_(n)} responsive tothe noise-corrupted read signal, the sequence of observed samples{y_(n)} forming a sequence of observed-sample subsequences {Y_(n)}, eachobserved-sample subsequence Y_(n) having an earliest observed sample anda latest observed sample; providing a sequence of expected samples{w_(n)}, the sequence of expected samples forming a sequence ofexpected-sample subsequences {W_(n)}, each expected-sample subsequenceW_(n) having an earliest expected sample and a latest expected sample;computing a sequence of channel metrics {Γ_(n)}, each channel metricΓ_(n) being a function of a distance determined from one of theobserved-sample subsequences Y_(n) to the corresponding expected-samplesubsequence W_(n), each channel metric Γ_(n) being independent of theearliest observed sample in every prior observed-sample subsequenceY_(n) and the earliest expected sample in every prior expected-samplesubsequence W_(n); computing a mean μ_(Γ) of the channel metrics{Γ_(n)}; computing a standard deviation σ_(Γ) of the channel metrics{Γ_(n)}; computing a ratio of the mean μ_(Γ) to the standard deviationσ_(Γ), the ratio (μ_(Γ)/σ_(Γ)) corresponding to a signal to noise ratio;and estimating the bit error rate from the ratio (μ_(Γ)/σ_(Γ)).
 8. Thedisk drive of claim 7, wherein the step of estimating the bit error ratecomprises the step of computing Q(μ_(Γ)/σ_(Γ)) where Q is a Gaussian Qfunction.
 9. A disk drive having a normal mode of operation and abuilt-in self-test mode of operation for producing a sequence of channelmetrics {Γ_(n)}, the disk drive comprising: a recording surface having aplurality of bit cells; a transducer for reading the plurality of bitcells to produce a noise-corrupted read signal; a sampler responsive tothe noise-corrupted read signal for generating a sequence of observedsamples {y_(n)}, the sequence of observed-samples {y_(n)} forming asequence of observed-sample sequences {Y_(n)}; an expected samplegenerator operative during the built-in self-test mode of operation forproviding a sequence of expected samples {w_(n)}, the sequence ofexpected samples forming a sequence of expected-sample subsequences{W_(n)}; channel metrics computer for computing a sequence of channelmetrics {Γ_(n)}, each channel metric Γ_(n) being a function of adistance determined from one of the observed-sample subsequences Y_(n)to the corresponding expected-sample sequence W_(n); and a ratiocomputer for computing a mean μ_(Γ) of the channel metrics Γ_(n), astandard deviation σ_(Γ) of the channel metrics Γ_(n), and a ratio ofthe mean μ_(Γ) to the standard deviation σ_(Γ), the ratio (μ_(Γ)/σ_(Γ))corresponding to a signal to noise ratio.
 10. The disk drive of claim 9,furthers comprising a bit error rate estimator for estimating a biterror rate of the disk drive by computing Q(μ_(Γ)/σ_(Γ)) where Q is aGaussian Q function.
 11. A disk drive having a normal mode of operationand a built-in self-test mode of operation for producing a sequence ofchannel metrics {Γ_(n)}, the disk drive comprising: a recording surfacehaving a plurality of bit cells; a transducer for reading the pluralityof bit cells to produce a noise-corrupted read signal; a samplerresponsive to the noise-corrupted read signal for generating a sequenceof observed samples {y_(n)}, the sequence of observed-samples {Y_(n)}forming a sequence of observed-sample sequences {Y_(n)}; an expectedsample generator operative during the built-in self-test mode ofoperation for providing a sequence of expected samples { w_(n)}, thesequence of expected samples forming a sequence of expected-samplesubsequences {W_(n)}; channel metrics computer for computing a sequenceof channel metrics {Γ_(n)}, each channel metric Γ_(n) being a functionof a distance determined from one of the observed-sample subsequencesY_(n) to the corresponding expected-sample sequence W_(n), the channelmetrics computer comprising: equation circuitry for implementing aplurality of equations to compute a plurality of different channelmetrics Γ_(n); and an equation selection matrix for selecting betweenthe different channel metrics Γ_(n) computed by the equation circuitry,the selection based on a selected number of the expected samples{w_(n)}, the equation matrix generating a selected channel metric Γ_(n);and an accumulator for accumulating the selected channel metric Γ_(n).12. A method for computing a sequence of channel metrics {Γ_(n)} forcharacterizing the performance of a disk drive comprising a recordingsurface having a plurality of bit cells and a transducer for reading theplurality of bit cells to produce a noise-corrupted read signal, themethod comprising the steps of: sampling the noise-corrupted read signalto generate a sequence of observed samples {y_(n)}, the sequence ofobserved-samples {y_(n)} forming a sequence of observed-sample sequences{Y_(n)}; generating, during the built-in self-test mode of operation, asequence of expected samples {w_(n)}, the sequence of expected samplesforming a sequence of expected-sample subsequences {W_(n)}; computing asequence of channel metrics {Γ_(n)}, each channel metric Γ_(n) being afunction of a distance determined from one of the observed-samplesubsequences Y_(n) to the corresponding expected-sample sequence W_(n),the step of computing the channel metrics comprising the steps of:computing a plurality of different channel metrics Γ_(n); and selectingbetween the different channel metrics Γ_(n) based on a selected numberof the expected samples {w_(n)} to generate a selected channel metricΓ_(n) ; and accumulating the selected channel metric Γ_(n).